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Challenge 24 - Modeling Churn Predictions II

Challenge 24 - Modeling Churn Predictions II
Challenge 24: Modeling Churn Predictions II Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going tochurn (that is, going to cancel their contracts) based on attributes of their accounts. One of your colleagues said that shewas able to achieve a bit over 95% accuracy for the test data without modifying the training data at all, and using all givenattributes exactly as they are. Again, the target class to be predicted is Churn (value 0 corresponds to customers that donot churn, and 1 corresponds to those who do). What model should you train over the training dataset to obtain thisaccuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of a mix of 1 component and 4 nodes.Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use allavailable attributes. DATA INPUT TRAIN MODELS & PREDICTION CHECK RESULTS Producebest ModelRead Testing Data ~20%ReadTraining Data ~80%Node 3Node 4AutoML CSV Reader CSV Reader Workflow Executor Scorer (JavaScript) Challenge 24: Modeling Churn Predictions II Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going tochurn (that is, going to cancel their contracts) based on attributes of their accounts. One of your colleagues said that shewas able to achieve a bit over 95% accuracy for the test data without modifying the training data at all, and using all givenattributes exactly as they are. Again, the target class to be predicted is Churn (value 0 corresponds to customers that donot churn, and 1 corresponds to those who do). What model should you train over the training dataset to obtain thisaccuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of a mix of 1 component and 4 nodes.Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use allavailable attributes. DATA INPUT TRAIN MODELS & PREDICTION CHECK RESULTS Producebest ModelRead Testing Data ~20%ReadTraining Data ~80%Node 3Node 4AutoML CSV Reader CSV Reader Workflow Executor Scorer (JavaScript)

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Created by: USER
Created at: 2022-07-02
On NodePit since: 2022-08-30
Last update: 2024-04-27
Created with KNIME version: v4.5.0
Tags: justknimeit-24

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